Open AccessComparison Analysis of Gait Classification For Human Motion Identification Using Embedded Computer
Author(s) -
Agung Nugroho Jati,
Astri Novianty,
Nanda Septiana,
Leni Widia Nasution
Publication year - 2018
Publication title -
international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.277
H-Index - 22
ISSN - 2088-8708
DOI - 10.11591/ijece.v8i6.pp5014-5020
Subject(s) - computer science , biometrics , gait , artificial intelligence , support vector machine , histogram , principal component analysis , pattern recognition (psychology) , gait analysis , identification (biology) , histogram of oriented gradients , feature extraction , k nearest neighbors algorithm , computer vision , physical medicine and rehabilitation , medicine , botany , image (mathematics) , biology
In this paper, it will be discussed about comparison between two kinds of classification methods in order to improve security system based of human gait. Gait is one of biometric methods which can be used to identify person. K-Nearest Neighbour has parallelly implemented with Support Vector Machine for classifying human gait in same basic system. Generally, system has been built using Histogram and Principal Component Analysis for gait detection and its feature extraction. Then, the result of the simulation showed that K-Nearest Neighbour is slower in processing and less accurate than Support Vector Machine in gait classification.